Chronological pattern indexing: An efficient feature extraction method for hand gesture recognition with Leap Motion
| Autor: | Med Salim Bouhlel, Safa Ameur, Anouar Ben Khalifa |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Feature vector Search engine indexing Feature extraction 020207 software engineering Pattern recognition 02 engineering and technology Set (abstract data type) Discriminative model Gesture recognition Signal Processing 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Representation (mathematics) Gesture |
| Zdroj: | Journal of Visual Communication and Image Representation. 70:102842 |
| ISSN: | 1047-3203 |
| DOI: | 10.1016/j.jvcir.2020.102842 |
| Popis: | Recently, Hand-Gesture-Recognition (HGR) systems has appreciably change the way of interaction between humans and computers thanks to advanced sensor technologies like the Leap-Motion-Controller (LMC). Despite the success achieved by many state-of-the-art methods, they have not worked on the rich temporal information existing in the sequential hand gesture data and characterizing the discriminative representation of different hand gesture classes. In this paper, we suggest a novel Chronological-Pattern-Indexing (CPI) approach which encodes the temporal orders of patterns for hand gesture time series data acquired by the LMC sensor. We extract a set of temporal patterns from different optimized projections. Then, we compare their temporal order and we encode the whole sequence with the index of the first coming pattern. We repeat these steps until we generate an efficient feature vector modeling the chronological dynamics of the hand gesture. The experiments demonstrate the potential of the proposed CPI approach for HGR systems. |
| Databáze: | OpenAIRE |
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